Femine 发表于 2025-3-26 22:15:24
http://reply.papertrans.cn/24/2343/234274/234274_31.png钢笔尖 发表于 2025-3-27 04:20:11
,UniCR: Universally Approximated Certified Robustness via Randomized Smoothing,-by-case analysis, (3) tightness validation of certified robustness, and (4) optimality validation of noise distributions used by randomized smoothing. We conduct extensive experiments to validate the above benefits of UniCR and the advantages of UniCR over state-of-the-art certified defenses against . perturbations.一个搅动不安 发表于 2025-3-27 09:02:23
,Learning Energy-Based Models with Adversarial Training,l-suited for image translation tasks, and exhibits strong out-of-distribution adversarial robustness. Our results demonstrate the viability of the AT approach to generative modeling, suggesting that AT is a competitive alternative approach to learning EBMs.BET 发表于 2025-3-27 09:49:29
https://doi.org/10.1007/978-3-031-20065-6Computer Science; Informatics; Conference Proceedings; Research; ApplicationsExplosive 发表于 2025-3-27 16:28:15
http://reply.papertrans.cn/24/2343/234274/234274_35.pngStress 发表于 2025-3-27 19:34:02
http://reply.papertrans.cn/24/2343/234274/234274_36.png多余 发表于 2025-3-28 01:17:01
Richard Simpson,Monika Zimmermannlizes several image transformation operations to improve the transferability of adversarial examples, which is effective, but fails to take the specific characteristic of the input image into consideration. In this work, we propose a novel architecture, called Adaptive Image Transformation Learner (lipoatrophy 发表于 2025-3-28 04:42:42
Richard Simpson,Monika ZimmermannWe find that only two modifications are absolutely necessary: 1) a multiplane image style generator branch which produces a set of alpha maps conditioned on their depth; 2) a pose-conditioned discriminator. We refer to the generated output as a ‘generative multiplane image’ (GMPI) and emphasize thatSTELL 发表于 2025-3-28 10:00:08
Kuwait and World Oil Developments,her prediction accuracy, few study the adversarial robustness of their methods. To bridge this gap, we propose to study the adversarial robustness of data-driven trajectory prediction systems. We devise an optimization-based adversarial attack framework that leverages a carefully-designed . to gener仪式 发表于 2025-3-28 13:58:39
http://reply.papertrans.cn/24/2343/234274/234274_40.png